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Learning-based parameter prediction for quality control in three-dimensional medical image compression Research Articles

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1169-1178 doi: 10.1631/FITEE.2000234

Abstract: is of vital importance in compressing three-dimensional (3D) medical imaging data. Optimal compression parameters need to be determined based on the specific quality requirement. In , regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme in predicting the ideal parameters for 3D cannot guarantee satisfactory results. In this paper we propose a parameter prediction scheme to achieve efficient . Its kernel is a support vector regression (SVR) based learning model that is capable of predicting the optimal QP from both video-based and structural image features extracted directly from raw data, avoiding time-consuming processes such as pre-encoding and iteration, which are often needed in existing techniques. Experimental results on several datasets verify that our approach outperforms current video-based methods.

Keywords： 医学图像压缩；高效视频编码（HEVC）；质量控制；基于学习方法    

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: models have achieved state-of-the-art performance in (NER); the good performance, however, relies heavily on substantial amounts of labeled data. In some specific areas such as medical, financial, and military domains, labeled data is very scarce, while is readily available. Previous studies have used to enrich word representations, but a large amount of entity information in is neglected, which may be beneficial to the NER task. In this study, we propose a for NER tasks, which learns to create high-quality labeled data by applying a pre-trained module to filter out erroneous pseudo labels. Pseudo labels are automatically generated for and used as if they were true labels. Our semi-supervised framework includes three steps: constructing an optimal single neural model for a specific NER task, learning a module that evaluates pseudo labels, and creating new labeled data and improving the NER model iteratively. Experimental results on two English NER tasks and one Chinese clinical NER task demonstrate that our method further improves the performance of the best single neural model. Even when we use only pre-trained static word embeddings and do not rely on any external knowledge, our method achieves comparable performance to those state-of-the-art models on the CoNLL-2003 and OntoNotes 5.0 English NER tasks.

Keywords： 命名实体识别；无标注数据；深度学习；半监督学习方法    

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A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps Article

Weichao Yue、 Weihua Gui、 Xiaofang Chen、 Zhaohui Zeng、 Yongfang Xie

Engineering 2019, Volume 5, Issue 6,   Pages 1060-1076 doi: 10.1016/j.eng.2019.10.005

Abstract:

In the aluminum reduction process, aluminum fluoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic efficiency. Making the decision on the amount of AlF3 addition (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into consideration a variety of interrelated functions; in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is difficult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have difficulty covering these complex causalities. In this work, a data and knowledge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy decision trees, which are used to amend the initial structure provided by experts. The state transition algorithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

Keywords： AlF 3 addition     Fuzzy cognitive maps     Learning algorithms     State transition algorithm     Fuzzy decision trees    

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The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis Review

Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao

Engineering 2021, Volume 7, Issue 6,   Pages 845-856 doi: 10.1016/j.eng.2020.07.030

Abstract:

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Keywords： Pavement monitoring and analysis     The state-of-the-art review     Intrusive sensing     Image processing techniques     Machine learning methods    

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Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives Review

Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li

Engineering 2019, Volume 5, Issue 4,   Pages 721-729 doi: 10.1016/j.eng.2019.04.012

Abstract:

Additive manufacturing (AM), also known as 3D printing, is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive manufacturing. However, AM processing parameters are difficult to tune, since they can exert a huge impact on the printed microstructure and on the performance of the subsequent products. It is a difficult task to build a process–structure–property–performance (PSPP) relationship for AM using traditional numerical and analytical models. Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical models. Among ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that is currently available, strong computational power, and sophisticated algorithm architecture. This paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain, including model design, in situ monitoring, and quality evaluation. Current challenges in applying NNs to AM and potential solutions for these problems are then outlined. Finally, future trends are proposed in order to provide an overall discussion of this interdisciplinary area.

Keywords： Additive manufacturing     3D printing     Neural network     Machine learning     Algorithm    

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Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Strategic Study of CAE 2004, Volume 6, Issue 11,   Pages 33-37

Abstract:

Based on optimization of constrained nonlinear programming, an approach of clustering center and a fuzzy membership function of pattern classification are derived from an objective function of the constrained nonlinear programming. An unsupervised algorithm with recursive expression and a fuzzy central cluster neural network are suggested in this paper. The fuzzy central cluster neural network proposed here can realize crisp decision or fuzzy decision in pattern classification.

Keywords： fuzzy sets     central cluster     pattern recognition     neural network    

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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers None

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 639-650 doi: 10.1631/FITEE.1700007

Abstract: We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrained maximum margin (CMM)’. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the constraint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential unconstrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.

Keywords： Discriminative learning     Statistical modeling     Bayesian pattern classifiers     Gaussian mixture models     UCI datasets    

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Fuzzy iterative learning control and numerical simulation of tall building seismic response control

Wang Quan,Wang Jianguo,Zhang Mingxiang

Strategic Study of CAE 2011, Volume 13, Issue 4,   Pages 81-86

Abstract:

With research into the fundamental ideas of self tuning control, fuzzy logic and iterative learning control (ILC), this paper provides a new type of fuzzy iterative learning control strategy to reduce the seismic response of tall building. It improves the robustness of the iterative learning control. The model of a seismically excited building in the second generation benchmark vibration control for buildings is studied, using the new control strategy to calculate the seismic response of the building. The result of simulation shows that fuzzy iterative learning control strategy can control the seismic response of the building effectively, and has advantages of simple and practical learning control law, high precision in trajectory and good robustness.

Keywords： tall building     seismic response     iterative learning control     fuzzy control    

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Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design Review

Teng Zhou, Zhen Song, Kai Sundmacher

Engineering 2019, Volume 5, Issue 6,   Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011

Abstract:

Materials development has historically been driven by human needs and desires, and this is likely to continue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-efficiency energy, personalized consumer products, secure food supplies, and professional healthcare. New functional materials that are made and tailored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic materials. Finally, concluding remarks and an outlook are provided.

Keywords： Big data     Data-driven     Machine learning     Materials screening     Materials design    

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Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives Research Article

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1390-1402 doi: 10.1631/FITEE.2300098

Abstract: (FL) is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their decentralized data. However, researchers working on FL face several unique challenges, especially in the context of heterogeneity. Heterogeneity in data distributions, computational capabilities, and scenarios among clients necessitates the development of customized models and objectives in FL. Unfortunately, existing works such as FedAvg may not effectively accommodate the specific needs of each client. To address the challenges arising from heterogeneity in FL, we provide an overview of the heterogeneities in data, model, and objective (DMO). Furthermore, we propose a novel framework called federated mutual learning (FML), which enables each client to train a personalized model that accounts for the data heterogeneity (DH). A "meme model" serves as an intermediary between the personalized and global models to address model heterogeneity (MH). We introduce a technique called deep mutual learning (DML) to transfer knowledge between these two models on local data. To overcome objective heterogeneity (OH), we design a shared global model that includes only certain parts, and the personalized model is task-specific and enhanced through mutual learning with the meme model. We evaluate the performance of FML in addressing DMO heterogeneities through experiments and compare it with other commonly used FL methods in similar scenarios. The results demonstrate that FML outperforms other methods and effectively addresses the DMO challenges encountered in the FL setting.

Keywords： Federated learning     Knowledge distillation     Privacy preserving     Heterogeneous environment    

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Two-level hierarchical feature learning for image classification Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 897-906 doi: 10.1631/FITEE.1500346

Abstract: In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.

Keywords： Transfer learning     Feature learning     Deep convolutional neural network     Hierarchical classification     Spectral clustering    

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The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Strategic Study of CAE 2009, Volume 11, Issue 2,   Pages 82-87

Abstract:

The data dimensionality reduction is the main method that can enhance the outliers mining efficiency based on higher- dimension data set. The research of detection of outliers based on manifold learning is proposed after analyzing the advantages and disadvantages of the classical outlier mining algorithm in the paper. Local Linear Embedding algorithm (LLE) is an effective technique for nonlinear dimensionality reduction in manifold learning. Compared with other dimensionality reduction algorithms, the advantage of the local Linear Embedding algorithm is that it only defines unique parameter, i. e. number of nearest neighbours. With the idea of Local Linear Embedding, the algorithm can select optimal parameter and regulate the distance among data set after data dimensionality reduction, so as to improve efficiency of detection of outliers. The algorithm determines weighted values by discretion formula of weighted outliers. Through these weighted values, the experts can identify the outliers easily. Simulation results illustrate that this algorithm is very efficient. Moreover, our method has the advantage of simple parameter estimation and low parameter sensitivity. Our method gives a new way for the solution of detection of outliers.

Keywords： manifold learning     detection of outliers     high dimensional data     dimensionality reduction     outliers    

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A machine learning approach to query generation in plagiarism source retrieval Article

Lei-lei KONG, Zhi-mao LU, Hao-liang QI, Zhong-yuan HAN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1556-1572 doi: 10.1631/FITEE.1601344

Abstract: Plagiarism source retrieval is the core task of plagiarism detection. It has become the standard for plagiarism detection to use the queries extracted from suspicious documents to retrieve the plagiarism sources. Generating queries from a suspicious document is one of the most important steps in plagiarism source retrieval. Heuristic-based query generation methods are widely used in the current research. Each heuristic-based method has its own advantages, and no one statistically outperforms the others on all suspicious document segments when generating queries for source retrieval. Further improvements on heuristic methods for source retrieval rely mainly on the experience of experts. This leads to difficulties in putting forward new heuristic methods that can overcome the shortcomings of the existing ones. This paper paves the way for a new statistical machine learning approach to select the best queries from the candidates. The statistical machine learning approach to query generation for source retrieval is formulated as a ranking framework. Specifically, it aims to achieve the optimal source retrieval performance for each suspicious document segment. The proposed method exploits learning to rank to generate queries from the candidates. To our knowledge, our work is the first research to apply machine learning methods to resolve the problem of query generation for source retrieval. To solve the essential problem of an absence of training data for learning to rank, the building of training samples for source retrieval is also conducted. We rigorously evaluate various aspects of the proposed method on the publicly available PAN source retrieval corpus. With respect to the established baselines, the experimental results show that applying our proposed query generation method based on machine learning yields statistically significant improvements over baselines in source retrieval effectiveness.

Keywords： Plagiarism detection     Source retrieval     Query generation     Machine learning     Learning to rank    

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Estimating Rainfall Intensity Using an Image-Based Deep Learning Model Article

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Engineering 2023, Volume 21, Issue 2,   Pages 162-174 doi: 10.1016/j.eng.2021.11.021

Abstract:

Urban flooding is a major issue worldwide, causing huge economic losses and serious threats to public safety. One promising way to mitigate its impacts is to develop a real-time flood risk management system; however, building such a system is often challenging due to the lack of high spatiotemporal rainfall data. While some approaches (i.e., ground rainfall stations or radar and satellite techniques) are available to measure and/or predict rainfall intensity, it is difficult to obtain accurate rainfall data with a desirable spatiotemporal resolution using these methods. This paper proposes an image-based deep learning model to estimate urban rainfall intensity with high spatial and temporal resolution. More specifically, a convolutional neural network (CNN) model called the image-based rainfall CNN (irCNN) model is developed using rainfall images collected from existing dense sensors (i.e., smart phones or transportation cameras) and their corresponding measured rainfall intensity values. The trained irCNN model is subsequently employed to efficiently estimate rainfall intensity based on the sensors' rainfall images. Synthetic rainfall data and real rainfall images are respectively utilized to explore the irCNN's accuracy in theoretically and practically simulating rainfall intensity. The results show that the irCNN model provides rainfall estimates with a mean absolute percentage error ranging between 13.5% and 21.9%, which exceeds the performance of other state-of-the-art modeling techniques in the literature. More importantly, the main feature of the proposed irCNN is its low cost in efficiently acquiring high spatiotemporal urban rainfall data. The irCNN model provides a promising alternative for estimating urban rainfall intensity, which can greatly facilitate the development of urban flood risk management in a real-time manner.

Keywords： Urban flooding     Rainfall images     Deep learning model     Convolutional neural networks (CNNs)     Rainfall intensity    

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Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology Article

Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang

Engineering 2023, Volume 28, Issue 9,   Pages 93-104 doi: 10.1016/j.eng.2021.12.014

Abstract:

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin (YHHRB). The values of the Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and percent bias (PBIAS) indicated the acceptable performance of the soil and water assessment tool (SWAT) model in the YHHRB. Nine descriptors belonging to the categories of climate, soil, vegetation, and topography were used to express the catchment characteristics related to the hydrological processes. The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models, including linear regression (LR) equations, support vector regression (SVR), random forest (RF), k-nearest neighbor (kNN), decision tree (DT), and radial basis function (RBF). Each of the 38 catchments was assumed to be an ungauged catchment in turn. Then, the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments. Furthermore, the similarity-based regionalization scheme was used for comparison with the regression-based approach. The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments. Compared with the traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships. The performances of different approaches were similar in humid regions, while the advantages of the machine learning techniques were more evident in arid regions. When the study area contained nested catchments, the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance. The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

Keywords： Parameters estimation     Ungauged catchments     Regionalization scheme     Machine learning algorithms     Soil and water assessment tool model    

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Title Author Date Type Operation

Learning-based parameter prediction for quality control in three-dimensional medical image compression

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Journal Article

Learning to select pseudo labels: a semi-supervised method for named entity recognition

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Journal Article

A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

Weichao Yue、 Weihua Gui、 Xiaofang Chen、 Zhaohui Zeng、 Yongfang Xie

Journal Article

The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao

Journal Article

Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives

Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li

Journal Article

Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Journal Article

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Journal Article

Fuzzy iterative learning control and numerical simulation of tall building seismic response control

Wang Quan,Wang Jianguo,Zhang Mingxiang

Journal Article

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design

Teng Zhou, Zhen Song, Kai Sundmacher

Journal Article

Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Journal Article

Two-level hierarchical feature learning for image classification

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Journal Article

The research of detection of outliers based on manifold lear ning

Xu Xuesong,Song Dongming,Zhang Xu,Xu Manwu,Liu Fengyu

Journal Article

A machine learning approach to query generation in plagiarism source retrieval

Lei-lei KONG, Zhi-mao LU, Hao-liang QI, Zhong-yuan HAN

Journal Article

Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Journal Article

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang

Journal Article